This file is designed to use CDC data to assess coronavirus disease burden by state, including creating and analyzing state-level clusters.
Through March 7, 2021, The COVID Tracking Project collected and integrated data on tests, cases, hospitalizations, deaths, and the like by state and date. The latest code for using this data is available in Coronavirus_Statistics_CTP_v004.Rmd.
The COVID Tracking Project suggest that US federal data sources are now sufficiently robust to be used for analyses that previously relied on COVID Tracking Project.
The code in this module builds on code available in _v005, with function and mapping files updated:
Broadly, the CDC data analyzed by this module includes:
The tidyverse package is loaded and functions are sourced:
# The tidyverse functions are routinely used without package::function format
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.0 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.1 ✔ tibble 3.1.8
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
library(geofacet)
# Functions are available in source file
source("./Generic_Added_Utility_Functions_202105_v001.R")
source("./Coronavirus_CDC_Daily_Functions_v002.R")
A series of mapping files are also available to allow for parameterized processing. Mappings include:
These default parameters are maintained in a separate .R file and can be sourced:
# These have been updated to _v003
source("./Coronavirus_CDC_Daily_Default_Mappings_v003.R")
The function is run on previously downloaded data:
The latest data are downloaded and processed:
readList <- list("cdcWeeklyBurden"="./RInputFiles/Coronavirus/CDC_dc_wkly_downloaded_230402.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_230402.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_230402.csv"
)
compareList <- list("cdcWeeklyBurden"=readFromRDS("cdc_daily_230302")$dfRaw$cdcWeeklyBurden,
"cdcHosp"=readFromRDS("cdc_daily_230302")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_230302")$dfRaw$vax
)
cdc_daily_230402 <- readRunCDCDaily(thruLabel="Mar 31, 2023",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_dc_wkly_downloaded_230402.csv
## Rows: 10020 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): date_updated, state, start_date, end_date
## dbl (6): tot_cases, new_cases, tot_deaths, new_deaths, new_historic_cases, n...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
## Warning: There was 1 warning in `summarize()`.
## ℹ In argument: `across(.cols = all_of(useVars), .fns = fn, ...)`.
## ℹ In group 1: `date = 2020-01-22`.
## Caused by warning:
## ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
## Supply arguments directly to `.fns` through an anonymous function instead.
##
## # Previously
## across(a:b, mean, na.rm = TRUE)
##
## # Now
## across(a:b, \(x) mean(x, na.rm = TRUE))
## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation ideoms with `aes()`
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2021-12-08 new_deaths 6465 8857 2392 0.31223078
## 2 2023-01-11 new_deaths 4108 4448 340 0.07947639
## 3 2022-06-01 new_deaths 1994 2103 109 0.05320967
## 4 2021-07-14 new_cases 251653 178256 73397 0.34145366
## 5 2021-07-07 new_cases 134473 109537 24936 0.20438507
## 6 2023-01-18 new_cases 338956 316627 22329 0.06811952
## 7 2021-06-02 new_cases 120788 113131 7657 0.06546711
## 8 2020-11-04 new_cases 652612 694701 42089 0.06247843
## 9 2020-08-12 new_cases 399531 377082 22449 0.05781258
## 10 2021-08-11 new_cases 851192 901672 50480 0.05759717
## 11 2020-08-19 new_cases 349383 330558 18825 0.05537245
## 12 2023-01-11 new_cases 432643 455887 23244 0.05232012
## 13 2021-06-30 new_cases 98741 93823 4918 0.05107912
## Warning in left_join(., df, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.
## Warning in left_join(., ref, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 MO tot_deaths 2041320 1889376 151944 0.077311499
## 2 MN tot_deaths 1323494 1311962 11532 0.008751427
## 3 RI tot_deaths 398207 399829 1622 0.004064980
## 4 MO tot_cases 134077761 135468146 1390385 0.010316499
## 5 FL tot_cases 556254722 561378228 5123506 0.009168495
## 6 DE tot_cases 24269412 24152458 116954 0.004830627
## 7 TX tot_cases 642477094 640839111 1637983 0.002552735
## 8 GU new_deaths 415 420 5 0.011976048
## 9 MO new_deaths 20214 20354 140 0.006901992
## 10 VT new_deaths 930 925 5 0.005390836
## 11 AR new_deaths 12930 12980 50 0.003859514
## 12 MN new_deaths 14448 14402 46 0.003188908
## 13 RI new_deaths 3870 3860 10 0.002587322
## 14 DE new_deaths 3329 3324 5 0.001503081
## 15 OK new_deaths 15411 15432 21 0.001361735
## 16 MO new_cases 1764510 1773152 8642 0.004885713
## 17 FL new_cases 7499410 7528420 29010 0.003860837
## 18 DE new_cases 331763 330817 946 0.002855504
## 19 KY new_cases 1716242 1713684 2558 0.001491577
## 20 OK new_cases 1282596 1284450 1854 0.001444462
## 21 MP new_cases 13680 13666 14 0.001023916
##
##
##
## Raw file for cdcWeeklyBurden:
## Rows: 10,020
## Columns: 10
## $ date_updated <chr> "01/23/2020", "01/30/2020", "02/06/2020", "02/13/2…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "A…
## $ start_date <chr> "01/16/2020", "01/23/2020", "01/30/2020", "02/06/2…
## $ date <date> 2020-01-22, 2020-01-29, 2020-02-05, 2020-02-12, 2…
## $ tot_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 63, 149, 235, 300, 337…
## $ new_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 52, 86, 86, 65, 37, 18…
## $ tot_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 7, 9, 9, 9, 10, 1…
## $ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 4, 2, 0, 0, 1, 0,…
## $ new_historic_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_historic_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_h_downloaded_230402.csv
## Rows: 60599 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl (1): geocoded_state
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 31
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2023-03-02 hosp_ped 1143 1038 105 0.09628611
## Warning in left_join(., df, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 VI inp 6233 6118 115 0.018621974
## 2 AK inp 71283 71837 554 0.007741755
## 3 KS inp 423272 422782 490 0.001158318
## 4 VI hosp_ped 158 147 11 0.072131148
## 5 AK hosp_ped 3229 3258 29 0.008940959
## 6 KS hosp_ped 5987 5969 18 0.003011040
## 7 PR hosp_ped 27722 27684 38 0.001371693
## 8 NH hosp_ped 1572 1570 2 0.001273074
## 9 IA hosp_ped 9618 9606 12 0.001248439
## 10 HI hosp_ped 4559 4554 5 0.001097333
## 11 WY hosp_ped 987 988 1 0.001012658
## 12 VI hosp_adult 5910 5806 104 0.017753499
## 13 AK hosp_adult 65959 66484 525 0.007927939
## 14 KS hosp_adult 393875 393403 472 0.001199068
## 15 PR hosp_adult 251141 250889 252 0.001003924
##
##
##
## Raw file for cdcHosp:
## Rows: 60,599
## Columns: 135
## $ state <chr> …
## $ date <date> …
## $ critical_staffing_shortage_today_yes <dbl> …
## $ critical_staffing_shortage_today_no <dbl> …
## $ critical_staffing_shortage_today_not_reported <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> …
## $ hospital_onset_covid <dbl> …
## $ hospital_onset_covid_coverage <dbl> …
## $ inpatient_beds <dbl> …
## $ inpatient_beds_coverage <dbl> …
## $ inpatient_beds_used <dbl> …
## $ inpatient_beds_used_coverage <dbl> …
## $ inp <dbl> …
## $ inpatient_beds_used_covid_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> …
## $ hosp_adult <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ hosp_ped <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ total_staffed_adult_icu_beds <dbl> …
## $ total_staffed_adult_icu_beds_coverage <dbl> …
## $ inpatient_beds_utilization <dbl> …
## $ inpatient_beds_utilization_coverage <dbl> …
## $ inpatient_beds_utilization_numerator <dbl> …
## $ inpatient_beds_utilization_denominator <dbl> …
## $ percent_of_inpatients_with_covid <dbl> …
## $ percent_of_inpatients_with_covid_coverage <dbl> …
## $ percent_of_inpatients_with_covid_numerator <dbl> …
## $ percent_of_inpatients_with_covid_denominator <dbl> …
## $ inpatient_bed_covid_utilization <dbl> …
## $ inpatient_bed_covid_utilization_coverage <dbl> …
## $ inpatient_bed_covid_utilization_numerator <dbl> …
## $ inpatient_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_covid_utilization <dbl> …
## $ adult_icu_bed_covid_utilization_coverage <dbl> …
## $ adult_icu_bed_covid_utilization_numerator <dbl> …
## $ adult_icu_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_utilization <dbl> …
## $ adult_icu_bed_utilization_coverage <dbl> …
## $ adult_icu_bed_utilization_numerator <dbl> …
## $ adult_icu_bed_utilization_denominator <dbl> …
## $ geocoded_state <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> …
## $ deaths_covid <dbl> …
## $ deaths_covid_coverage <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> …
## $ icu_patients_confirmed_influenza <dbl> …
## $ icu_patients_confirmed_influenza_coverage <dbl> …
## $ previous_day_admission_influenza_confirmed <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage <dbl> …
## $ previous_day_deaths_covid_and_influenza <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> …
## $ previous_day_deaths_influenza <dbl> …
## $ previous_day_deaths_influenza_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> …
## $ all_pediatric_inpatient_beds <dbl> …
## $ all_pediatric_inpatient_beds_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage <dbl> …
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/vaxData_downloaded_230402.csv
## Rows: 38104 Columns: 109
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Date, Location
## dbl (107): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 5
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## Warning in left_join(., df, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.
##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 38,104
## Columns: 109
## $ date <date> 2023-03-29, 2023-03-29, 2023-0…
## $ MMWR_week <dbl> 13, 13, 13, 13, 13, 13, 13, 13,…
## $ state <chr> "BP2", "AS", "HI", "IH2", "RI",…
## $ Distributed <dbl> 418430, 128480, 4598400, 407234…
## $ Distributed_Janssen <dbl> 16200, 600, 124900, 111800, 905…
## $ Distributed_Moderna <dbl> 172520, 25000, 1503820, 1528780…
## $ Distributed_Pfizer <dbl> 198750, 100770, 2362220, 193963…
## $ Distributed_Novavax <dbl> 0, 0, 4400, 8300, 4400, 3100, 4…
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K <dbl> 0, 271101, 324775, 195521, 3293…
## $ Distributed_Per_100k_5Plus <dbl> 0, 298028, 345575, 0, 347230, 4…
## $ Distributed_Per_100k_12Plus <dbl> 0, 348024, 379685, 0, 376187, 4…
## $ Distributed_Per_100k_18Plus <dbl> 0, 410021, 412042, 0, 408146, 4…
## $ Distributed_Per_100k_65Plus <dbl> 0, 3919460, 1712960, 0, 1865370…
## $ vxa <dbl> 347647, 115241, 3507351, 255996…
## $ Administered_5Plus <dbl> 347645, 115218, 3484460, 254425…
## $ Administered_12Plus <dbl> 347645, 102427, 3345431, 243141…
## $ Administered_18Plus <dbl> 347645, 85702, 3132541, 2221391…
## $ Administered_65Plus <dbl> 11918, 9482, 987470, 478068, 72…
## $ Administered_Janssen <dbl> 14831, 580, 71624, 42014, 67173…
## $ Administered_Moderna <dbl> 156444, 25359, 1170251, 1098391…
## $ Administered_Pfizer <dbl> 166491, 87454, 1952507, 1249079…
## $ Administered_Novavax <dbl> 0, 0, 358, 62, 475, 228, 1719, …
## $ Administered_Unk_Manuf <dbl> 38, 1280, 1264, 1168, 2958, 566…
## $ Admin_Per_100k <dbl> 0, 243166, 247717, 122909, 2510…
## $ Admin_Per_100k_5Plus <dbl> 0, 267265, 261861, 0, 262891, 3…
## $ Admin_Per_100k_12Plus <dbl> 0, 277452, 276229, 0, 275080, 3…
## $ Admin_Per_100k_18Plus <dbl> 0, 273502, 280693, 0, 280703, 3…
## $ Admin_Per_100k_65Plus <dbl> 0, 289262, 367844, 0, 389569, 4…
## $ Recip_Administered <dbl> 347647, 115689, 3542243, 255996…
## $ Administered_Dose1_Recip <dbl> 155680, 46206, 1296078, 1164523…
## $ Administered_Dose1_Pop_Pct <dbl> 0.0, 95.0, 91.5, 55.9, 95.0, 95…
## $ Administered_Dose1_Recip_5Plus <dbl> 155678, 46185, 1284179, 1155760…
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 0.0, 95.0, 95.0, 0.0, 95.0, 95.…
## $ Administered_Dose1_Recip_12Plus <dbl> 155678, 39582, 1219812, 1098519…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 0.0, 95.0, 95.0, 0.0, 95.0, 95.…
## $ Administered_Dose1_Recip_18Plus <dbl> 155678, 32786, 1128879, 998919,…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 0.0, 95.0, 95.0, 0.0, 95.0, 95.…
## $ Administered_Dose1_Recip_65Plus <dbl> 4224, 3313, 281004, 187075, 234…
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 0.0, 95.0, 95.0, 0.0, 95.0, 95.…
## $ vxc <dbl> 141689, 42479, 1155548, 919151,…
## $ vxcpoppct <dbl> 0.0, 89.6, 81.6, 44.1, 87.9, 90…
## $ Series_Complete_5Plus <dbl> 141688, 42478, 1149155, 916394,…
## $ Series_Complete_5PlusPop_Pct <dbl> 0.0, 95.0, 86.4, 0.0, 92.1, 95.…
## $ Series_Complete_12Plus <dbl> 141688, 36383, 1091967, 872683,…
## $ Series_Complete_12PlusPop_Pct <dbl> 0.0, 95.0, 90.2, 0.0, 95.0, 95.…
## $ vxcgte18 <dbl> 141688, 29946, 1009600, 792582,…
## $ vxcgte18pct <dbl> 0.0, 95.0, 90.5, 0.0, 95.0, 95.…
## $ vxcgte65 <dbl> 3819, 2994, 257765, 148580, 200…
## $ vxcgte65pct <dbl> 0.0, 91.3, 95.0, 0.0, 95.0, 95.…
## $ Series_Complete_Janssen <dbl> 14150, 585, 66365, 39327, 61635…
## $ Series_Complete_Moderna <dbl> 57123, 9540, 380958, 410201, 31…
## $ Series_Complete_Pfizer <dbl> 70416, 31720, 704866, 468368, 5…
## $ Series_Complete_Novavax <dbl> 0, 0, 133, 18, 125, 86, 630, 32…
## $ Series_Complete_Unk_Manuf <dbl> 0, 634, 354, 73, 777, 1438, 559…
## $ Series_Complete_Janssen_5Plus <dbl> 14149, 585, 66337, 39300, 61634…
## $ Series_Complete_Moderna_5Plus <dbl> 57123, 9540, 379050, 409220, 30…
## $ Series_Complete_Pfizer_5Plus <dbl> 70416, 31719, 703282, 467783, 5…
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 0, 634, 353, 73, 760, 1411, 550…
## $ Series_Complete_Janssen_12Plus <dbl> 14149, 585, 66334, 39299, 61634…
## $ Series_Complete_Moderna_12Plus <dbl> 57123, 9539, 378746, 408993, 30…
## $ Series_Complete_Pfizer_12Plus <dbl> 70416, 25626, 646452, 424304, 5…
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 0, 633, 304, 69, 716, 1139, 526…
## $ Series_Complete_Janssen_18Plus <dbl> 14149, 584, 66144, 39254, 61604…
## $ Series_Complete_Moderna_18Plus <dbl> 57123, 9516, 377810, 408550, 30…
## $ Series_Complete_Pfizer_18Plus <dbl> 70416, 19217, 565253, 344693, 4…
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 0, 629, 264, 69, 667, 842, 499,…
## $ Series_Complete_Janssen_65Plus <dbl> 201, 28, 11854, 3508, 6866, 321…
## $ Series_Complete_Moderna_65Plus <dbl> 1837, 1217, 113726, 84001, 8940…
## $ Series_Complete_Pfizer_65Plus <dbl> 1781, 1732, 132111, 61063, 1040…
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 0, 17, 36, 5, 183, 219, 97, 128…
## $ Additional_Doses <dbl> 53622, 24680, 690868, 345445, 5…
## $ Additional_Doses_Vax_Pct <dbl> 37.8, 58.1, 59.8, 37.6, 58.4, 5…
## $ Additional_Doses_5Plus <dbl> 53622, 24680, 690646, 345311, 5…
## $ Additional_Doses_5Plus_Vax_Pct <dbl> 37.8, 58.1, 60.1, 37.7, 58.7, 5…
## $ Additional_Doses_12Plus <dbl> 53622, 24648, 676291, 335147, 5…
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 37.8, 67.7, 61.9, 38.4, 60.6, 5…
## $ Additional_Doses_18Plus <dbl> 53622, 21198, 641485, 310374, 5…
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 37.8, 70.8, 63.5, 39.2, 62.0, 5…
## $ Additional_Doses_50Plus <dbl> 16806, 8437, 396324, 182866, 30…
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 54.9, 79.6, 77.4, 47.7, 73.5, 6…
## $ Additional_Doses_65Plus <dbl> 2697, 2412, 218746, 82374, 1627…
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 70.6, 80.6, 84.9, 55.4, 81.1, 7…
## $ Additional_Doses_Moderna <dbl> 31606, 4945, 288011, 154519, 22…
## $ Additional_Doses_Pfizer <dbl> 21350, 19729, 396026, 188102, 3…
## $ Additional_Doses_Janssen <dbl> 643, 2, 6539, 2525, 5352, 2113,…
## $ Additional_Doses_Unk_Manuf <dbl> 23, 4, 245, 294, 210, 501, 247,…
## $ Second_Booster <dbl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Second_Booster_50Plus <dbl> 5026, 2274, 239196, 80425, 1721…
## $ Second_Booster_50Plus_Vax_Pct <dbl> 29.9, 27.0, 60.4, 44.0, 55.8, 5…
## $ Second_Booster_65Plus <dbl> 1115, 875, 149385, 41110, 10615…
## $ Second_Booster_65Plus_Vax_Pct <dbl> 41.3, 36.3, 68.3, 49.9, 65.2, 6…
## $ Second_Booster_Janssen <dbl> 3, 0, 123, 21, 178, 93, 196, 26…
## $ Second_Booster_Moderna <dbl> 2634, 532, 144967, 48240, 10103…
## $ Second_Booster_Pfizer <dbl> 6153, 1907, 161732, 65694, 1262…
## $ Second_Booster_Unk_Manuf <dbl> 3, 0, 54, 256, 108, 373, 61, 22…
## $ Administered_Bivalent <dbl> 9837, 525, 311091, 169098, 2565…
## $ Admin_Bivalent_PFR <dbl> 9507, 328, 169384, 107715, 1438…
## $ Admin_Bivalent_MOD <dbl> 330, 197, 141707, 61383, 112675…
## $ Dist_Bivalent_PFR <dbl> 19560, 1610, 357640, 321450, 29…
## $ Dist_Bivalent_MOD <dbl> 11400, 500, 245420, 162380, 189…
## $ Bivalent_Booster_5Plus <dbl> 9773, 563, 312151, 168051, 2664…
## $ Bivalent_Booster_5Plus_Pop_Pct <dbl> 0.0, 1.3, 23.5, 0.0, 26.5, 33.4…
## $ Bivalent_Booster_12Plus <dbl> 9773, 560, 304879, 161297, 2607…
## $ Bivalent_Booster_12Plus_Pop_Pct <dbl> 0.0, 1.5, 25.2, 0.0, 28.1, 34.9…
## $ Bivalent_Booster_18Plus <dbl> 9773, 555, 294343, 149113, 2519…
## $ Bivalent_Booster_18Plus_Pop_Pct <dbl> 0.0, 1.8, 26.4, 0.0, 29.5, 35.6…
## $ Bivalent_Booster_65Plus <dbl> 677, 197, 138250, 45820, 109055…
## $ Bivalent_Booster_65Plus_Pop_Pct <dbl> 0.0, 6.0, 51.5, 0.0, 58.3, 55.7…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 8.02e+9 1.06e+8 1.04e+8 1121270 9853
## 2 after 7.95e+9 1.05e+8 1.03e+8 1114704 8517
## 3 pctchg 8.63e-3 4.92e-3 1.21e-2 0.00586 0.136
## Warning: Using `all_of()` outside of a selecting function was deprecated in tidyselect
## 1.2.0.
## ℹ See details at
## <https://tidyselect.r-lib.org/reference/faq-selection-context.html>
## Warning: Using `by = character()` to perform a cross join was deprecated in dplyr 1.1.0.
## ℹ Please use `cross_join()` instead.
##
## Processed for cdcWeeklyBurden:
## Rows: 59,313
## Columns: 6
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01-26…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK",…
## $ tot_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tot_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 5.76e+7 5.07e+7 1511938 60599
## 2 after 5.73e+7 5.04e+7 1483681 57789
## 3 pctchg 5.37e-3 5.12e-3 0.0187 0.0464
##
##
## Processed for cdcHosp:
## Rows: 57,789
## Columns: 5
## $ date <date> 2021-02-18, 2021-02-13, 2021-02-05, 2021-02-02, 2021-01-26…
## $ state <chr> "SD", "NM", "CT", "MA", "ME", "MD", "RI", "MT", "DE", "ND",…
## $ inp <dbl> 90, 334, 979, 1518, 221, 2180, 423, 181, 510, 162, 434, 449…
## $ hosp_adult <dbl> 87, 327, 973, 1501, 220, 2161, 416, 178, 501, 159, 432, 446…
## $ hosp_ped <dbl> 3, 7, 6, 17, 1, 19, 7, 3, 9, 3, 2, 3, 24, 10, 73, 15, 5, 4,…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…² n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.54e+11 1.84e+11 1636331. 4.63e+10 2.40e+6 1.70e+11 1.92e+6 3.81e+4
## 2 after 2.19e+11 8.89e+10 1368749. 2.24e+10 2.12e+6 8.21e+10 1.63e+6 3.01e+4
## 3 pctchg 5.18e- 1 5.16e- 1 0.164 5.16e- 1 1.16e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹vxcgte65pct, ²vxcgte18pct
##
##
## Processed for vax:
## Rows: 42,636
## Columns: 9
## $ date <date> 2020-12-14, 2020-12-15, 2020-12-16, 2020-12-17, 2020-12-1…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK"…
## $ vxa <dbl> 0, 0, 0, 2, 2, 1607, 4239, 5125, 5615, 6822, 8578, 10612, …
## $ vxc <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcpoppct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte65 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte65pct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte18 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte18pct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
##
## Integrated per capita data file:
## Rows: 59,679
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
saveToRDS(cdc_daily_230402, ovrWriteError=FALSE)
##
## File already exists: ./RInputFiles/Coronavirus/cdc_daily_230402.RDS
##
## Not replacing the existing file since ovrWrite=FALSE
## NULL
The latest hospitalization data is also downloaded and processed:
# Run for latest data, save as RDS
indivHosp_20230403 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20230403.csv")
##
## File ./RInputFiles/Coronavirus/HHS_Hospital_20230403.csv already exists
## File will not be downloaded since ovrWrite is not TRUE
## Rows: 261207 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): hospital_pk, state, ccn, hospital_name, address, city, zip, hosp...
## dbl (114): total_beds_7_day_avg, all_adult_hospital_beds_7_day_avg, all_adu...
## lgl (2): is_metro_micro, is_corrected
## date (1): collection_week
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 261,207
## Columns: 128
## $ hospital_pk <chr> …
## $ collection_week <date> …
## $ state <chr> …
## $ ccn <chr> …
## $ hospital_name <chr> …
## $ address <chr> …
## $ city <chr> …
## $ zip <chr> …
## $ hospital_subtype <chr> …
## $ fips_code <chr> …
## $ is_metro_micro <lgl> …
## $ total_beds_7_day_avg <dbl> …
## $ all_adult_hospital_beds_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> …
## $ inpatient_beds_used_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> …
## $ inpatient_beds_used_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ inpatient_beds_7_day_avg <dbl> …
## $ total_icu_beds_7_day_avg <dbl> …
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> …
## $ icu_beds_used_7_day_avg <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> …
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> …
## $ total_beds_7_day_sum <dbl> …
## $ all_adult_hospital_beds_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> …
## $ inpatient_beds_used_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> …
## $ inpatient_beds_used_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ inpatient_beds_7_day_sum <dbl> …
## $ total_icu_beds_7_day_sum <dbl> …
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> …
## $ icu_beds_used_7_day_sum <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> …
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> …
## $ total_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> …
## $ inpatient_beds_used_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ inpatient_beds_used_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ inpatient_beds_7_day_coverage <dbl> …
## $ total_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> …
## $ icu_beds_used_7_day_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> …
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> …
## $ previous_day_covid_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> …
## $ previous_day_total_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> …
## $ geocoded_hospital_address <chr> …
## $ hhs_ids <chr> …
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> …
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> …
## $ is_corrected <lgl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_avg <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_sum <dbl> …
## $ all_pediatric_inpatient_beds_7_day_avg <dbl> …
## $ all_pediatric_inpatient_beds_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_beds_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_avg <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_sum <dbl> …
##
## Hospital Subtype Counts:
## # A tibble: 4 × 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 4857
## 2 Critical Access Hospitals 70233
## 3 Long Term 17536
## 4 Short Term 168581
##
## Records other than 50 states and DC
## # A tibble: 5 × 2
## state n
## <chr> <int>
## 1 AS 29
## 2 GU 123
## 3 MP 40
## 4 PR 2556
## 5 VI 98
##
## Record types for key metrics
## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = name`.
## # A tibble: 10 × 5
## name `NA` Posit…¹ Value…² Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 77420 183398 389 261207
## 2 all_adult_hospital_inpatient_bed_occupied_7_day… 30117 211729 19361 261207
## 3 icu_beds_used_7_day_avg 33204 199916 28087 261207
## 4 inpatient_beds_7_day_avg 8352 251849 1006 261207
## 5 inpatient_beds_used_7_day_avg 8352 231713 21142 261207
## 6 inpatient_beds_used_covid_7_day_avg 1763 173895 85549 261207
## 7 staffed_icu_adult_patients_confirmed_and_suspec… 30086 156464 74657 261207
## 8 total_adult_patients_hospitalized_confirmed_and… 27571 157031 76605 261207
## 9 total_beds_7_day_avg 53399 207570 238 261207
## 10 total_icu_beds_7_day_avg 3330 244695 13182 261207
## # … with abbreviated variable names ¹Positive, ²`Value -999999`
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20230403, ovrWriteError=FALSE)
##
## File already exists: ./RInputFiles/Coronavirus/indivHosp_20230403.RDS
##
## Not replacing the existing file since ovrWrite=FALSE
## NULL
Post-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_230402 <- postProcessCDCDaily(cdc_daily_230402,
dataThruLabel="Mar 2023",
keyDatesBurden=c("2023-03-29", "2022-06-30",
"2021-12-31", "2021-06-30"
),
keyDatesVaccine=c("2023-03-29", "2021-12-31",
"2021-08-31", "2021-03-31"
),
returnData=TRUE
)
## Joining with `by = join_by(state)`
##
## *** File has been checked for uniqueness by: state date name
## Warning: Removed 24 rows containing missing values (`geom_line()`).
## Warning: Removed 24 rows containing missing values (`position_stack()`).
## Warning: Removed 24 rows containing missing values (`position_stack()`).
## Warning: Removed 9 rows containing missing values (`geom_line()`).
## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.
## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.
## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.
## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.
## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.
## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.
# Create hospitalized per capita data
hospPerCap_230402 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_230402,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)
## Warning: Removed 18 rows containing missing values (`geom_line()`).
burdenPivotList_230402$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")
## # A tibble: 18 × 6
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_con… 5.13e4 60599
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_con… 3.20e5 60599
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_con… 4.59e5 60599
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_con… 5.40e5 60599
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_con… 8.69e5 60599
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_con… 1.18e6 60599
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_con… 1.23e6 60599
## 8 adult confirmed 80+ previous_day_admission_adult_covid_con… 1.17e6 60599
## 9 adult suspected 0-19 previous_day_admission_adult_covid_sus… 4.47e4 60599
## 10 adult suspected 20-29 previous_day_admission_adult_covid_sus… 2.98e5 60599
## 11 adult suspected 30-39 previous_day_admission_adult_covid_sus… 3.93e5 60599
## 12 adult suspected 40-49 previous_day_admission_adult_covid_sus… 3.98e5 60599
## 13 adult suspected 50-59 previous_day_admission_adult_covid_sus… 6.32e5 60599
## 14 adult suspected 60-69 previous_day_admission_adult_covid_sus… 8.86e5 60599
## 15 adult suspected 70-79 previous_day_admission_adult_covid_sus… 8.73e5 60599
## 16 adult suspected 80+ previous_day_admission_adult_covid_sus… 8.03e5 60599
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covid… 2.03e5 60599
## 18 ped suspected 0-19 previous_day_admission_pediatric_covid… 4.75e5 60599
saveToRDS(burdenPivotList_230402, ovrWriteError=FALSE)
##
## File already exists: ./RInputFiles/Coronavirus/burdenPivotList_230402.RDS
##
## Not replacing the existing file since ovrWrite=FALSE
## NULL
saveToRDS(hospPerCap_230402, ovrWriteError=FALSE)
##
## File already exists: ./RInputFiles/Coronavirus/hospPerCap_230402.RDS
##
## Not replacing the existing file since ovrWrite=FALSE
## NULL
Peaks and valleys of key metrics are also updated:
peakValleyCDCDaily(cdc_daily_230402)
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 20 rows containing missing values (`geom_line()`).
## Warning: Removed 20 rows containing missing values (`geom_line()`).
## # A tibble: 10,236 × 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # … with 10,226 more rows
Hospital data are pieced together as needed:
# Create modified hospital data
multiSourceHosp_20230402 <- multiSourceDataCombine(list(readFromRDS("indivHosp_20220704"),
readFromRDS("indivHosp_20230403")
),
timeVec=as.Date("2022-01-01")
)
The updated hospital data are then plotted:
# Run hospital plots
modStateHosp_20230402 <- hospitalCapacityCDCDaily(multiSourceHosp_20230402,
plotSub="Aug 2020 to Mar 2023\nOld data used pre-2022"
)
The latest data are downloaded and processed:
readList <- list("cdcWeeklyBurden"="./RInputFiles/Coronavirus/CDC_dc_wkly_downloaded_230502.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_230502.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_230502.csv"
)
compareList <- list("cdcWeeklyBurden"=readFromRDS("cdc_daily_230402")$dfRaw$cdcWeeklyBurden,
"cdcHosp"=readFromRDS("cdc_daily_230402")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_230402")$dfRaw$vax
)
cdc_daily_230502 <- readRunCDCDaily(thruLabel="Apr 30, 2023",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)
## Rows: 10260 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): date_updated, state, start_date, end_date
## dbl (6): tot_cases, new_cases, tot_deaths, new_deaths, new_historic_cases, n...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
## Warning: There was 1 warning in `summarize()`.
## ℹ In argument: `across(.cols = all_of(useVars), .fns = fn, ...)`.
## ℹ In group 1: `date = 2020-01-22`.
## Caused by warning:
## ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
## Supply arguments directly to `.fns` through an anonymous function instead.
##
## # Previously
## across(a:b, mean, na.rm = TRUE)
##
## # Now
## across(a:b, \(x) mean(x, na.rm = TRUE))
## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation ideoms with `aes()`
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-03-11 tot_deaths 33 14 19 0.80851064
## 2 2020-03-18 tot_deaths 202 140 62 0.36257310
## 3 2020-03-25 tot_deaths 1387 1259 128 0.09674981
## 4 2020-02-26 tot_cases 80 72 8 0.10526316
## 5 2020-03-11 new_deaths 22 8 14 0.93333333
## 6 2021-12-08 new_deaths 8816 6465 2351 0.30770238
## 7 2020-03-18 new_deaths 169 126 43 0.29152542
## 8 2022-05-04 new_deaths 2427 2742 315 0.12188044
## 9 2022-05-11 new_deaths 2157 2401 244 0.10706450
## 10 2022-04-20 new_deaths 2716 2955 239 0.08428849
## 11 2022-04-27 new_deaths 2676 2903 227 0.08137659
## 12 2022-08-10 new_deaths 3377 3624 247 0.07056135
## 13 2022-05-18 new_deaths 2122 2275 153 0.06959290
## 14 2022-08-03 new_deaths 3369 3572 203 0.05849301
## 15 2020-03-25 new_deaths 1185 1119 66 0.05729167
## 16 2022-05-25 new_deaths 2511 2648 137 0.05311107
## 17 2022-06-01 new_deaths 1896 1994 98 0.05038560
## 18 2020-03-04 new_cases 97 108 11 0.10731707
## Warning in left_join(., df, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.
## Warning in left_join(., ref, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 WA tot_deaths 1292526 1139242 153284 0.126067947
## 2 AR tot_cases 80951600 82279688 1328088 0.016272469
## 3 MO new_deaths 22734 20311 2423 0.112579858
## 4 DE new_deaths 3379 3352 27 0.008022582
## 5 OK new_deaths 15572 15612 40 0.002565418
## 6 MS new_deaths 13419 13402 17 0.001267663
## 7 VT new_deaths 942 941 1 0.001062135
## 8 AR new_cases 990092 1007357 17265 0.017287050
## 9 KY new_cases 1732457 1724772 7685 0.004445757
## 10 OK new_cases 1293792 1295832 2040 0.001575518
## 11 MS new_cases 994313 993035 1278 0.001286136
##
##
##
## Raw file for cdcWeeklyBurden:
## Rows: 10,260
## Columns: 10
## $ date_updated <chr> "01/23/2020", "01/30/2020", "02/06/2020", "02/13/2…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "A…
## $ start_date <chr> "01/16/2020", "01/23/2020", "01/30/2020", "02/06/2…
## $ date <date> 2020-01-22, 2020-01-29, 2020-02-05, 2020-02-12, 2…
## $ tot_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 63, 149, 235, 300, 337…
## $ new_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 52, 86, 86, 65, 37, 18…
## $ tot_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 7, 9, 9, 9, 10, 1…
## $ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 4, 2, 0, 0, 1, 0,…
## $ new_historic_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_historic_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## Rows: 62162 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl (1): geocoded_state
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 29
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2023-04-01 hosp_ped 812 901 89 0.10391127
## 2 2023-04-02 hosp_ped 825 898 73 0.08473593
## Warning in left_join(., df, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 VI inp 6142 6274 132 0.021262887
## 2 VI hosp_ped 147 159 12 0.078431373
## 3 NV hosp_ped 6991 7029 38 0.005420827
## 4 VT hosp_ped 855 859 4 0.004667445
## 5 AK hosp_ped 3283 3294 11 0.003344990
## 6 DE hosp_ped 9129 9156 27 0.002953240
## 7 KY hosp_ped 28401 28484 83 0.002918168
## 8 MS hosp_ped 15392 15371 21 0.001365276
## 9 WY hosp_ped 990 991 1 0.001009591
## 10 VI hosp_adult 5830 5950 120 0.020373514
##
##
##
## Raw file for cdcHosp:
## Rows: 62,162
## Columns: 135
## $ state <chr> …
## $ date <date> …
## $ critical_staffing_shortage_today_yes <dbl> …
## $ critical_staffing_shortage_today_no <dbl> …
## $ critical_staffing_shortage_today_not_reported <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> …
## $ hospital_onset_covid <dbl> …
## $ hospital_onset_covid_coverage <dbl> …
## $ inpatient_beds <dbl> …
## $ inpatient_beds_coverage <dbl> …
## $ inpatient_beds_used <dbl> …
## $ inpatient_beds_used_coverage <dbl> …
## $ inp <dbl> …
## $ inpatient_beds_used_covid_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> …
## $ hosp_adult <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ hosp_ped <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ total_staffed_adult_icu_beds <dbl> …
## $ total_staffed_adult_icu_beds_coverage <dbl> …
## $ inpatient_beds_utilization <dbl> …
## $ inpatient_beds_utilization_coverage <dbl> …
## $ inpatient_beds_utilization_numerator <dbl> …
## $ inpatient_beds_utilization_denominator <dbl> …
## $ percent_of_inpatients_with_covid <dbl> …
## $ percent_of_inpatients_with_covid_coverage <dbl> …
## $ percent_of_inpatients_with_covid_numerator <dbl> …
## $ percent_of_inpatients_with_covid_denominator <dbl> …
## $ inpatient_bed_covid_utilization <dbl> …
## $ inpatient_bed_covid_utilization_coverage <dbl> …
## $ inpatient_bed_covid_utilization_numerator <dbl> …
## $ inpatient_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_covid_utilization <dbl> …
## $ adult_icu_bed_covid_utilization_coverage <dbl> …
## $ adult_icu_bed_covid_utilization_numerator <dbl> …
## $ adult_icu_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_utilization <dbl> …
## $ adult_icu_bed_utilization_coverage <dbl> …
## $ adult_icu_bed_utilization_numerator <dbl> …
## $ adult_icu_bed_utilization_denominator <dbl> …
## $ geocoded_state <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> …
## $ deaths_covid <dbl> …
## $ deaths_covid_coverage <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> …
## $ icu_patients_confirmed_influenza <dbl> …
## $ icu_patients_confirmed_influenza_coverage <dbl> …
## $ previous_day_admission_influenza_confirmed <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage <dbl> …
## $ previous_day_deaths_covid_and_influenza <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> …
## $ previous_day_deaths_influenza <dbl> …
## $ previous_day_deaths_influenza_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> …
## $ all_pediatric_inpatient_beds <dbl> …
## $ all_pediatric_inpatient_beds_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage <dbl> …
## Rows: 38360 Columns: 109
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Date, Location
## dbl (107): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## Warning in left_join(., df, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.
##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 38,360
## Columns: 109
## $ date <date> 2023-04-26, 2023-04-26, 2023-0…
## $ MMWR_week <dbl> 17, 17, 17, 17, 17, 17, 17, 17,…
## $ state <chr> "WI", "MH", "PA", "IA", "MP", "…
## $ Distributed <dbl> 16390495, 162240, 42655195, 936…
## $ Distributed_Janssen <dbl> 457200, 12800, 1569200, 293100,…
## $ Distributed_Moderna <dbl> 5144600, 74500, 13941120, 30779…
## $ Distributed_Pfizer <dbl> 8183105, 53540, 21178525, 45778…
## $ Distributed_Novavax <dbl> 22100, 1100, 77100, 22000, 200,…
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K <dbl> 281506, 208693, 333192, 296761,…
## $ Distributed_Per_100k_5Plus <dbl> 298446, 234458, 352404, 316379,…
## $ Distributed_Per_100k_12Plus <dbl> 327824, 282801, 384922, 349852,…
## $ Distributed_Per_100k_18Plus <dbl> 359769, 341349, 419530, 385591,…
## $ Distributed_Per_100k_65Plus <dbl> 1611270, 4347270, 1782230, 1693…
## $ vxa <dbl> 12419813, 101022, 27536533, 612…
## $ Administered_5Plus <dbl> 12323643, 97254, 27313374, 6078…
## $ Administered_12Plus <dbl> 11907256, 85856, 26362356, 5882…
## $ Administered_18Plus <dbl> 11242331, 73524, 24878639, 5554…
## $ Administered_65Plus <dbl> 3933430, 4718, 8471200, 2004872…
## $ Administered_Janssen <dbl> 338955, 3099, 803119, 181028, 1…
## $ Administered_Moderna <dbl> 4215718, 66804, 9687787, 217083…
## $ Administered_Pfizer <dbl> 6528721, 24899, 14561549, 31323…
## $ Administered_Novavax <dbl> 1519, 0, 2381, 770, 1, 474, 366…
## $ Administered_Unk_Manuf <dbl> 5153, 5, 1979, 2765, 9, 2788, 1…
## $ Admin_Per_100k <dbl> 213310, 129947, 215096, 194031,…
## $ Admin_Per_100k_5Plus <dbl> 224395, 140545, 225655, 205407,…
## $ Admin_Per_100k_12Plus <dbl> 238155, 149656, 237895, 219806,…
## $ Admin_Per_100k_18Plus <dbl> 246768, 154693, 244691, 228743,…
## $ Admin_Per_100k_65Plus <dbl> 386676, 126420, 353946, 362575,…
## $ Recip_Administered <dbl> 12423939, 101077, 27595351, 613…
## $ Administered_Dose1_Recip <dbl> 4385177, 44548, 11654826, 22346…
## $ Administered_Dose1_Pop_Pct <dbl> 75.3, 57.3, 91.0, 70.8, 90.3, 6…
## $ Administered_Dose1_Recip_5Plus <dbl> 4339949, 41959, 11550924, 22149…
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 79.0, 60.6, 95.0, 74.8, 95.0, 7…
## $ Administered_Dose1_Recip_12Plus <dbl> 4155511, 35402, 11098829, 21262…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 83.1, 61.7, 95.0, 79.4, 95.0, 7…
## $ Administered_Dose1_Recip_18Plus <dbl> 3879760, 29330, 10416722, 19848…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 85.2, 61.7, 95.0, 81.7, 95.0, 8…
## $ Administered_Dose1_Recip_65Plus <dbl> 1080934, 1626, 2915906, 564261,…
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 43.6, 95.0, 95.0, 87.9, 9…
## $ vxc <dbl> 3980224, 35878, 9435863, 203581…
## $ vxcpoppct <dbl> 68.4, 46.2, 73.7, 64.5, 84.9, 5…
## $ Series_Complete_5Plus <dbl> 3953783, 34907, 9365799, 202431…
## $ Series_Complete_5PlusPop_Pct <dbl> 72.0, 50.4, 77.4, 68.4, 91.3, 6…
## $ Series_Complete_12Plus <dbl> 3788449, 30561, 8996519, 194539…
## $ Series_Complete_12PlusPop_Pct <dbl> 75.8, 53.3, 81.2, 72.7, 93.8, 6…
## $ vxcgte18 <dbl> 3538185, 26009, 8440810, 181782…
## $ vxcgte18pct <dbl> 77.7, 54.7, 83.0, 74.9, 95.0, 6…
## $ vxcgte65 <dbl> 1008642, 1458, 2442959, 532654,…
## $ vxcgte65pct <dbl> 95.0, 39.1, 95.0, 95.0, 84.1, 8…
## $ Series_Complete_Janssen <dbl> 303882, 2997, 742926, 165248, 1…
## $ Series_Complete_Moderna <dbl> 1389641, 23275, 3373880, 741446…
## $ Series_Complete_Pfizer <dbl> 2274436, 9586, 5299518, 1123041…
## $ Series_Complete_Novavax <dbl> 597, 0, 882, 227, 1, 221, 136, …
## $ Series_Complete_Unk_Manuf <dbl> 1853, 5, 914, 1157, 3, 2035, 36…
## $ Series_Complete_Janssen_5Plus <dbl> 303866, 2995, 742869, 165246, 1…
## $ Series_Complete_Moderna_5Plus <dbl> 1376509, 22334, 3327352, 736619…
## $ Series_Complete_Pfizer_5Plus <dbl> 2270984, 9573, 5293790, 1121080…
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 1827, 5, 907, 1146, 3, 2034, 36…
## $ Series_Complete_Janssen_12Plus <dbl> 303850, 2993, 742793, 165241, 1…
## $ Series_Complete_Moderna_12Plus <dbl> 1375439, 22299, 3323605, 736006…
## $ Series_Complete_Pfizer_12Plus <dbl> 2106840, 5264, 4928341, 1042891…
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 1723, 5, 899, 1034, 3, 2012, 31…
## $ Series_Complete_Janssen_18Plus <dbl> 303638, 2982, 742204, 165136, 1…
## $ Series_Complete_Moderna_18Plus <dbl> 1374651, 22248, 3319423, 735431…
## $ Series_Complete_Pfizer_18Plus <dbl> 1857807, 775, 4377515, 916134, …
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 1517, 4, 827, 900, 3, 1896, 272…
## $ Series_Complete_Janssen_65Plus <dbl> 30989, 123, 93526, 14716, 120, …
## $ Series_Complete_Moderna_65Plus <dbl> 480190, 1304, 1139942, 278489, …
## $ Series_Complete_Pfizer_65Plus <dbl> 496936, 31, 1209044, 239170, 25…
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 454, 0, 328, 244, 0, 898, 38, 7…
## $ Additional_Doses <dbl> 2429374, 17330, 4486440, 117299…
## $ Additional_Doses_Vax_Pct <dbl> 61.0, 48.3, 47.5, 57.6, 54.3, 4…
## $ Additional_Doses_5Plus <dbl> 2426612, 17269, 4478372, 117230…
## $ Additional_Doses_5Plus_Vax_Pct <dbl> 61.4, 49.5, 47.8, 57.9, 54.5, 4…
## $ Additional_Doses_12Plus <dbl> 2372823, 16828, 4388878, 114933…
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 62.6, 55.1, 48.8, 59.1, 59.0, 4…
## $ Additional_Doses_18Plus <dbl> 2268903, 15432, 4207790, 110280…
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 64.1, 59.3, 49.9, 60.7, 61.9, 5…
## $ Additional_Doses_50Plus <dbl> 1485438, 4369, 2881528, 745903,…
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 75.5, 72.2, 60.9, 73.9, 74.9, 6…
## $ Additional_Doses_65Plus <dbl> 846948, 1098, 1680708, 439523, …
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 84.0, 75.3, 68.8, 82.5, 81.7, 7…
## $ Additional_Doses_Moderna <dbl> 965050, 13715, 1883230, 505586,…
## $ Additional_Doses_Pfizer <dbl> 1435914, 3537, 2538678, 652564,…
## $ Additional_Doses_Janssen <dbl> 27840, 78, 64268, 14391, 217, 2…
## $ Additional_Doses_Unk_Manuf <dbl> 474, 0, 219, 418, 0, 416, 254, …
## $ Second_Booster <dbl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Second_Booster_50Plus <dbl> 939055, 2018, 1443765, 459549, …
## $ Second_Booster_50Plus_Vax_Pct <dbl> 63.2, 46.2, 50.1, 61.6, 26.8, 4…
## $ Second_Booster_65Plus <dbl> 609115, 532, 962775, 308307, 98…
## $ Second_Booster_65Plus_Vax_Pct <dbl> 71.9, 48.5, 57.3, 70.1, 38.3, 5…
## $ Second_Booster_Janssen <dbl> 257, 0, 527, 109, 1, 321, 123, …
## $ Second_Booster_Moderna <dbl> 474352, 3198, 744447, 223548, 5…
## $ Second_Booster_Pfizer <dbl> 754714, 1571, 1059865, 355060, …
## $ Second_Booster_Unk_Manuf <dbl> 148, 0, 69, 240, 0, 175, 58, 5,…
## $ Administered_Bivalent <dbl> 1329420, 5804, 2479433, 633567,…
## $ Admin_Bivalent_PFR <dbl> 874690, 3786, 1614515, 438061, …
## $ Admin_Bivalent_MOD <dbl> 454730, 2018, 864918, 195506, 5…
## $ Dist_Bivalent_PFR <dbl> 1755930, 14700, 4049030, 983130…
## $ Dist_Bivalent_MOD <dbl> 827560, 5600, 1840220, 408980, …
## $ Bivalent_Booster_5Plus <dbl> 1313948, 5681, 2383302, 626574,…
## $ Bivalent_Booster_5Plus_Pop_Pct <dbl> 23.9, 8.2, 19.7, 21.2, 9.9, 13.…
## $ Bivalent_Booster_12Plus <dbl> 1282077, 5176, 2329557, 613751,…
## $ Bivalent_Booster_12Plus_Pop_Pct <dbl> 25.6, 9.0, 21.0, 22.9, 10.6, 14…
## $ Bivalent_Booster_18Plus <dbl> 1235965, 4486, 2252226, 593246,…
## $ Bivalent_Booster_18Plus_Pop_Pct <dbl> 27.1, 9.4, 22.2, 24.4, 11.2, 15…
## $ Bivalent_Booster_65Plus <dbl> 591007, 356, 1095875, 305171, 8…
## $ Bivalent_Booster_65Plus_Pop_Pct <dbl> 58.1, 9.5, 45.8, 55.2, 21.3, 36…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 8.43e+9 1.11e+8 1.04e+8 1129008 10089
## 2 after 8.36e+9 1.10e+8 1.03e+8 1122409 8721
## 3 pctchg 8.81e-3 4.95e-3 1.22e-2 0.00584 0.136
## Warning: Using `all_of()` outside of a selecting function was deprecated in tidyselect
## 1.2.0.
## ℹ See details at
## <https://tidyselect.r-lib.org/reference/faq-selection-context.html>
## Warning: Using `by = character()` to perform a cross join was deprecated in dplyr 1.1.0.
## ℹ Please use `cross_join()` instead.
##
## Processed for cdcWeeklyBurden:
## Rows: 60,741
## Columns: 6
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01-26…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK",…
## $ tot_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tot_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 5.81e+7 5.11e+7 1533306 62162
## 2 after 5.78e+7 5.09e+7 1504781 59268
## 3 pctchg 5.36e-3 5.11e-3 0.0186 0.0466
##
##
## Processed for cdcHosp:
## Rows: 59,268
## Columns: 5
## $ date <date> 2021-02-23, 2021-02-05, 2021-02-02, 2021-01-26, 2021-01-20…
## $ state <chr> "RI", "AK", "KY", "LA", "MD", "SD", "ME", "AL", "MO", "OR",…
## $ inp <dbl> 207, 43, 1365, 1502, 2180, 189, 227, 3340, 2597, 512, 449, …
## $ hosp_adult <dbl> 207, 43, 1350, 1488, 2161, 189, 225, 3309, 2524, 507, 446, …
## $ hosp_ped <dbl> 0, 0, 15, 14, 19, 0, 2, 31, 73, 5, 3, 1, 3, 70, 4, 21, 2, 8…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…² n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.59e+11 1.86e+11 1653142. 4.67e+10 2.42e+6 1.71e+11 1.94e+6 3.84e+4
## 2 after 2.21e+11 8.98e+10 1382689. 2.26e+10 2.14e+6 8.29e+10 1.64e+6 3.03e+4
## 3 pctchg 5.18e- 1 5.16e- 1 0.164 5.16e- 1 1.16e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹vxcgte65pct, ²vxcgte18pct
##
##
## Processed for vax:
## Rows: 44,064
## Columns: 9
## $ date <date> 2020-12-14, 2020-12-15, 2020-12-16, 2020-12-17, 2020-12-1…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK"…
## $ vxa <dbl> 0, 0, 0, 2, 2, 1607, 4239, 5125, 5615, 6822, 8578, 10612, …
## $ vxc <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcpoppct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte65 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte65pct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte18 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte18pct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
##
## Integrated per capita data file:
## Rows: 61,158
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
saveToRDS(cdc_daily_230502, ovrWriteError=FALSE)
The latest hospitalization data is also downloaded and processed:
# Run for latest data, save as RDS
indivHosp_20230503 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20230503.csv")
##
## File ./RInputFiles/Coronavirus/HHS_Hospital_20230503.csv already exists
## File will not be downloaded since ovrWrite is not TRUE
## Rows: 285856 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): hospital_pk, state, ccn, hospital_name, address, city, zip, hosp...
## dbl (114): total_beds_7_day_avg, all_adult_hospital_beds_7_day_avg, all_adu...
## lgl (2): is_metro_micro, is_corrected
## date (1): collection_week
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 285,856
## Columns: 128
## $ hospital_pk <chr> …
## $ collection_week <date> …
## $ state <chr> …
## $ ccn <chr> …
## $ hospital_name <chr> …
## $ address <chr> …
## $ city <chr> …
## $ zip <chr> …
## $ hospital_subtype <chr> …
## $ fips_code <chr> …
## $ is_metro_micro <lgl> …
## $ total_beds_7_day_avg <dbl> …
## $ all_adult_hospital_beds_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> …
## $ inpatient_beds_used_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> …
## $ inpatient_beds_used_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ inpatient_beds_7_day_avg <dbl> …
## $ total_icu_beds_7_day_avg <dbl> …
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> …
## $ icu_beds_used_7_day_avg <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> …
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> …
## $ total_beds_7_day_sum <dbl> …
## $ all_adult_hospital_beds_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> …
## $ inpatient_beds_used_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> …
## $ inpatient_beds_used_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ inpatient_beds_7_day_sum <dbl> …
## $ total_icu_beds_7_day_sum <dbl> …
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> …
## $ icu_beds_used_7_day_sum <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> …
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> …
## $ total_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> …
## $ inpatient_beds_used_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ inpatient_beds_used_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ inpatient_beds_7_day_coverage <dbl> …
## $ total_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> …
## $ icu_beds_used_7_day_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> …
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> …
## $ previous_day_covid_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> …
## $ previous_day_total_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> …
## $ geocoded_hospital_address <chr> …
## $ hhs_ids <chr> …
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> …
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> …
## $ is_corrected <lgl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_avg <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_sum <dbl> …
## $ all_pediatric_inpatient_beds_7_day_avg <dbl> …
## $ all_pediatric_inpatient_beds_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_beds_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_avg <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_sum <dbl> …
##
## Hospital Subtype Counts:
## # A tibble: 4 × 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 5313
## 2 Critical Access Hospitals 76897
## 3 Long Term 19259
## 4 Short Term 184387
##
## Records other than 50 states and DC
## # A tibble: 5 × 2
## state n
## <chr> <int>
## 1 AS 32
## 2 GU 116
## 3 MP 39
## 4 PR 2737
## 5 VI 110
##
## Record types for key metrics
## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = name`.
## # A tibble: 10 × 5
## name `NA` Posit…¹ Value…² Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 87037 198399 420 285856
## 2 all_adult_hospital_inpatient_bed_occupied_7_day… 31637 232980 21239 285856
## 3 icu_beds_used_7_day_avg 34854 220054 30948 285856
## 4 inpatient_beds_7_day_avg 8673 276090 1093 285856
## 5 inpatient_beds_used_7_day_avg 8673 254066 23117 285856
## 6 inpatient_beds_used_covid_7_day_avg 1778 189444 94634 285856
## 7 staffed_icu_adult_patients_confirmed_and_suspec… 31599 172182 82075 285856
## 8 total_adult_patients_hospitalized_confirmed_and… 28937 171663 85256 285856
## 9 total_beds_7_day_avg 61699 223911 246 285856
## 10 total_icu_beds_7_day_avg 3437 267929 14490 285856
## # … with abbreviated variable names ¹Positive, ²`Value -999999`
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20230503, ovrWriteError=FALSE)
Post-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_230502 <- postProcessCDCDaily(cdc_daily_230502,
dataThruLabel="Apr 2023",
keyDatesBurden=c("2023-04-26", "2022-06-30",
"2021-12-31", "2021-06-30"
),
keyDatesVaccine=c("2023-04-26", "2021-12-31",
"2021-08-31", "2021-03-31"
),
returnData=TRUE
)
## Joining with `by = join_by(state)`
##
## *** File has been checked for uniqueness by: state date name
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## Warning: Removed 24 rows containing missing values (`geom_line()`).
## Warning: Removed 24 rows containing missing values (`position_stack()`).
## Warning: Removed 24 rows containing missing values (`position_stack()`).
## Warning: Removed 9 rows containing missing values (`geom_line()`).
## Warning: There was 1 warning in `filter()`.
## ℹ In argument: `date %in% all_of(keyDates)`.
## Caused by warning:
## ! Using `all_of()` outside of a selecting function was deprecated in tidyselect
## 1.2.0.
## ℹ See details at
## <https://tidyselect.r-lib.org/reference/faq-selection-context.html>
## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.
## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.
## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.
## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.
## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.
## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.
# Create hospitalized per capita data
hospPerCap_230502 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_230502,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)
## Warning: Removed 18 rows containing missing values (`geom_line()`).
burdenPivotList_230502$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")
## # A tibble: 18 × 6
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_con… 5.15e4 62162
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_con… 3.22e5 62162
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_con… 4.61e5 62162
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_con… 5.42e5 62162
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_con… 8.73e5 62162
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_con… 1.19e6 62162
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_con… 1.24e6 62162
## 8 adult confirmed 80+ previous_day_admission_adult_covid_con… 1.19e6 62162
## 9 adult suspected 0-19 previous_day_admission_adult_covid_sus… 4.54e4 62162
## 10 adult suspected 20-29 previous_day_admission_adult_covid_sus… 3.02e5 62162
## 11 adult suspected 30-39 previous_day_admission_adult_covid_sus… 3.98e5 62162
## 12 adult suspected 40-49 previous_day_admission_adult_covid_sus… 4.04e5 62162
## 13 adult suspected 50-59 previous_day_admission_adult_covid_sus… 6.42e5 62162
## 14 adult suspected 60-69 previous_day_admission_adult_covid_sus… 9.03e5 62162
## 15 adult suspected 70-79 previous_day_admission_adult_covid_sus… 8.90e5 62162
## 16 adult suspected 80+ previous_day_admission_adult_covid_sus… 8.19e5 62162
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covid… 2.05e5 62162
## 18 ped suspected 0-19 previous_day_admission_pediatric_covid… 4.84e5 62162
saveToRDS(burdenPivotList_230502, ovrWriteError=FALSE)
saveToRDS(hospPerCap_230502, ovrWriteError=FALSE)